What is Model drift?
Model drift is the degradation or change in an AI model's performance over time because the data or real-world conditions it faces differ from those it was trained on. It includes data drift, where inputs change, and concept drift, where the relationship between inputs and outcomes changes.
For example, a fraud model trained before a new payment method appears may miss new patterns. Drift is one reason AI governance includes monitoring after launch and scheduled reassessment. Thresholds, owners and responses to drift alerts should be defined in advance.